Adding a segmentation version that doesn't use query string passing

src/pg/sql/05_segmentation.sql

@@ -1,3 +1,41 @@
+
+CREATE OR REPLACE FUNCTION
+  CDB_CreateAndPredictSegment(
+    target NUMERIC[],
+    features NUMERIC[],
+    target_features NUMERIC[],
+    target_ids NUMERIC[],
+    n_estimators INTEGER DEFAULT 1200,
+    max_depth INTEGER DEFAULT 3,
+    subsample DOUBLE PRECISION DEFAULT 0.5,
+    learning_rate DOUBLE PRECISION DEFAULT 0.01,
+    min_samples_leaf INTEGER DEFAULT 1
+  )
+RETURNS TABLE( cartodb_id Numeric, prediction Numeric , accuracy Numeric)
+AS $$
+    import numpy as np 
+    import plpy 
+
+    from crankshaft.segmentation import create_and_predict_segment_agg
+    model_params = { 'n_estimators' : n_estimators, 
+                     'max_depth' : max_depth, 
+                     'subsample' : subsample, 
+                     'learning_rate' : learning_rate, 
+                     'min_samples_leaf' : min_samples_leaf} 
+  
+    def unpack2D(data):
+        dimension = data.pop(0)
+        a = np.array(data, dtype=float)
+        return a.reshape(len(a)/dimension, dimension)
+
+    return create_and_predict_segment_agg( np.array(target, dtype=float), 
+            unpack2D(features), 
+            unpack2D(target_features), 
+            target_ids,
+            model_params)
+
+$$ Language plpythonu;
+
 CREATE OR REPLACE FUNCTION
   CDB_CreateAndPredictSegment (
       query TEXT,
@@ -16,4 +54,3 @@ AS $$
   model_params = {'n_estimators': n_estimators, 'max_depth':max_depth, 'subsample' : subsample, 'learning_rate': learning_rate, 'min_samples_leaf' : min_samples_leaf} 
   return create_and_predict_segment(query,variable_name,target_table, model_params)
 $$ LANGUAGE plpythonu;
-